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Data journalism (DJ) stands out as a distinguished contemporary form of news storytelling in which data are simplified and communicated via visuals. It can disseminate knowledge on complex phenomena and contribute to the advancement of journalism. Understanding the motives of readers' DJ consumption is vital to the understanding of three focal elements in the journalism equation: society, journalists, and news-media institutions. This paper fills a gap in the knowledge about studies in DJ – audience interrelationship, contributing to the understanding of the twinning relationship between domesticating everchanging communication technologies and DJ consumption. The theoretical framework draws on news consumption and domestication theory in examining the way media and communication students in Palestinian universities (hereinafter MC students) interact with DJ-based stories communicated via social media platforms. Surveying MC students (N = 99) at four prominent Palestinian universities in the West Bank, the paper explores the motivations behind MC students' DJ consumption, and how recent media technologies might induce its levels of consumption. Targeting Facebook, results show that MC students' engagement with DJ stories is primarily induced by their interest in the topics presented, with social and human stories as primary topics. Other inducers included visuals, proximity to topics discussed, and familiarity with the publishing source. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is impacting and has the potential to upend entire business models and structures. The adoption of such new technologies to support newsgathering processes is established practice for newsrooms. For AI specifically, we are seeing a new era of AI‐assisted journalism emerge with trust in the AI‐driven analyses and accuracy of results as core tenets. In Part I of this position paper, we discuss the contributions of six recently published research papers co‐authored by Bloomberg's Artificial Intelligence Engineering team that show the intricacies of training AI models for reliable newsgathering processes. The papers investigate (a) the creation of models for updated headline generation, showing that headline generation models benefit from access to the past state of the article, (b) sequentially controlled text generation, which is a novel task and we show that in general, more structured awareness results in higher control accuracy and grammatical coherence, (c) chart summarization, which looks into identifying the key message and generating sentences that describe salient information in the multimodal documents, (d) a semistructured natural language inference task to develop a framework for data augmentation for tabular inference, (e) the introduction of a human‐annotated dataset (ENTSUM) for controllable summarization with a focus on named entities as the aspect to control, and (f) a novel defense mechanism against adversarial attacks (ATINTER). We also examine Bloomberg's research work, building its own internal, not‐for‐commercial‐use large language model, BloombergGPT, and training it with the goal of demonstrating support for a wide range of tasks within the financial industry. In Part II, we analyze the evolution of automation tasks in the Bloomberg newsroom that led to the creation of Bloomberg's News Innovation Lab. Technology‐assisted content creation has been a reality at Bloomberg News for nearly a decade and has evolved from rules‐based headline generation from structured files to the constant exploration of potential ways to assist story creation and storytelling in the financial domain. The Lab now oversees the operation of hundreds of software bots that create semi‐ and fully automated stories of financial relevance, providing journalists with depth in terms of data and analysis, speed in terms of reacting to breaking news, and transparency to corners of the financial world where data investigation is a gigantic undertaking. The Lab recently introduced new tools that provide journalists with the ability to explore automation on demand while it continues to experiment with ways to assist story production. In Part III, we conceptually discuss the transformative impact that generative AI can have in any newsroom, along with considerations about the technology's shortcomings in its current state of development. As with any revolutionary new technology, as well as with exciting research opportunities, part of the challenge is balancing any potential positive and negative impacts on society. We offer our principles and guidelines used to inform our approach to experimenting with the new generative AI technologies. Bloomberg News' style guide reminds us that our "journalism is aimed at possibly the most sophisticated audience in the world, for whom accuracy is essential." [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive bibliometric review of the development of artificial intelligence (AI) in journalism based on the analysis of 331 articles indexed in the Scopus database between 2019 and 2023. This research combines bibliometric approaches and quantitative content analysis to provide an in-depth conceptual and structural overview of the field. In addition to descriptive measures, co-citation and co-word analyses are also presented to reveal patterns and trends in AI- and journalism-related research. The results show a significant increase in the number of articles published each year, with the largest contributions coming from the United States, Spain, and the United Kingdom, serving as the most productive countries. Terms such as "fake news", "algorithms", and "automated journalism" frequently appear in the reviewed articles, reflecting the main topics of concern in this field. Furthermore, ethical aspects of journalism were highlighted in every discussion, indicating a new paradigm that needs to be considered for the future development of journalism studies and professionalism. [ABSTRACT FROM AUTHOR]

Copyright of Annals of Cultural Studies / Roczniki Kulturoznawcze is the property of John Paul II Catholic University of Lublin, Institute of Cultural Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AI Journalism has been increasing its market penetration and extending its reach in newsrooms and classrooms from the Global North to the Global South, though with varying levels of resources. AI development in journalism innovation comes on the back of trends in AI research efforts and the labor market: research publications on AI have doubled; the share of AI jobs among job postings in Singapore skyrocketed and surpassed US, Canada, UK, and Australia; AI itself is contributing to the development of AI; and annual global corporate investment in AI rose to $176.5 billion in 2021 from $6.1 billion in 2013. With increasing academic and business interests, Asian newsrooms are taking either rapid or measured strides in developing their suite of AI experiences that leverage automation and a business model that is flexible enough to absorb shocks from constant digital transformation and disruption. Using database and web search, as well as a review of financial statements and national AI strategies, the study contributes a comparative overview of AI perceptions, plans, content, and ethics in online news platforms in a developed and a developing country in Southeast Asia, Singapore, and the Philippines. [ABSTRACT FROM AUTHOR]

The algorithmic turn has fundamentally transformed journalistic work. Academic interest in the implication of automated algorithms for journalism has grown hand-in-hand with their everyday use. This paper presents a literature review of peer-reviewed research reports (N = 62) on automated algorithms in the context of journalistic work. Our review focuses on the first decade (2010–2019) during which automated journalism gained traction. The study identifies the most prominent perspectives or themes that studies in automated journalism have explored and the future directions for research that researchers have proposed. Based on the analysis, the dominant themes that studies in automated journalism have covered include (1) testing and developing algorithmic tools, (2) developing practices and policies for journalistic work, (3) attitudes and technology acceptance, and (4) societal and macro-level discourses concerning AI and journalism. The new directions for research that studies on automated algorithms have recognized relate to (1) target groups and stakeholders—that is, who to study in the future; (2) emergent themes and phenomena—that is, what to study in the future; and (3) approaches and methodologies—that is, how to study these topics in the future. These findings help create a holistic picture of possible future directions for the field. [ABSTRACT FROM AUTHOR]

Copyright of Horyzonty Wychowania is the property of Jesuit University Ignatianum in Krakow and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article delves into the strategies of different Chinese news organizations' (e.g., state-owned media, we-media, private news organizations) engagement with audiences in data journalism, aiming to attain dual legitimacy (identity legitimacy and institutional legitimacy) within the unique landscape of the digital media era in China. Utilizing the lens of organizational legitimacy, qualitative interviews were conducted with 26 Chinese data news practitioners. The findings reveal that news entities have adopted "restrictive involvement" and "substitutive involvement" strategies to limit audience engagement to superficial interactions within the consumption process of data journalism. Identity legitimacy has traditionally served as the primary incentive for news organizations to engage audiences, while institutional legitimacy has constrained the forms and degrees of audience engagement. The study posits that audience deployment by news organizations is more of a rhetorical maneuver than a practical engagement, symbolically involving audiences in China's data news production. This research contributes an institutional perspective to the understanding of data journalism and audience engagement dynamics, shedding light on the intricate interactions between news entities, audiences, technology and the state within China's context. [ABSTRACT FROM AUTHOR]

This study explores what motivates data journalists to engage with audiences and their strategies for incorporating audiences into their work. Building on scholarship on audience engagement and participatory journalism, we investigate how data journalists perceive the role of audience; the stage of the reporting process at which the audience is engaged; and how optimistically or sceptically data journalists view the audience's capacity to contribute to the data journalism reporting process. Using a news media logics theoretical framework, we find data journalists are primarily motivated by a mixture of professional and audience logics The mixture of these logics aligns with their goals to establish institutional identity and legitimization in society, but increasingly data journalists also emphasise hopes for greater authentic participation from their audiences across the reporting process. Analysis of data gathered from in-depth interviews with data journalists from 34 countries provides a better and broader empirical context for explanation of data journalists' goals for audience engagement, the tools they use to connect with audiences, and the degree to which those goals are met. Our findings contribute to a clearer explanation of audience engagement motivations and strategies in data journalism and the similarities that emerge across a broad geographic array of data journalism work. With a focus on crowdsourcing, data disclosure, interactivity, and news dissemination as forms of audience engagement, we synthesise a portrait of attitudes about audience engagement from the data journalist's perspective and highlight global similarities. [ABSTRACT FROM AUTHOR]

Data journalism is increasingly vital in our data-driven society, requiring professionals to gather, analyze, and visualize data for public understanding. While scholars recognize its significance, the audience-centric aspects remain underexplored. This study focuses on Eco-Nai+, a digital geo-journalism platform aiming to be Nigeria's first. It provides interactive data access via web and mobile apps and tools for geospatial data handling. Employing a multi-method approach, including document analysis, interviews, and platform analysis, this study examines how geo-data enhances data-driven storytelling, fosters cooperation and co-creation in data collection, and creates a new income stream for news organizations. From a business standpoint, Eco-Nai + functions as a "newstech" company, capitalizing on digital age challenges. It offers journalists a means to inform and engage the public and policymakers in vital environmental discussions, especially in environments with limited open data sources. Crowdsourcing data is critical in this context, where open data and freedom of information legislation are lacking. This research contributes to the data journalism discourse by exploring audience involvement in data-driven storytelling and advocating for public participation in data journalism projects to enhance reporting and understand data's societal role. It also underscores the value of geojournalism skills in delivering spatial products and thematic maps, adding context and insights beyond numerical data. Eco-Nai + exemplifies the potential for data journalism to bridge information gaps and drive meaningful conversations in an increasingly data-centric world. [ABSTRACT FROM AUTHOR]

Drawing from 18 audience interviews, this article examines audience perception of the trustworthiness of COVID-19 data visualisations in UK newspaper coverage. The findings suggest that overall, the participants viewed the selected COVID-19 data visualisations as largely trustworthy. Their perception was unaffected by the types of data visualisations. The trustworthiness of data visualisations had no clear connection with their likability and learnability. Instead, the participants' trust was influenced by the perceived problematic presentation of data visualisations, such as the inappropriate use of bars to represent data or the failure to present data in context. It was also affected by the participants' understanding of the problems about data (production and presentation), their assessment of the credibility of data sources and news outlets, and their personal lived experiences and information gained from other sources. All of these were related to the social context surrounding data and data visualisations, rather than merely the content of data visualisations. The findings reveal that the social construction nature of data and data visualisations creates a space for the participants to question data visualisations' trustworthiness. The close connection between trust in data visualisations and trust in data, a socially constructed product, suggests that the trustworthiness of data visualisations transcends the control of journalists and news media, extending to the context of data and its visualisations. This qualitative research reveals the importance of context to audience trust in data visualisations in the UK. [ABSTRACT FROM AUTHOR]

With their advertising and audience revenues in decline, local news organizations have been experiencing comparatively high degrees of disruption in recent years. Artificial Intelligence (AI) offers opportunities for local news organizations to better cope with the economic challenges they face. However, local news organizations need to carefully prioritize where AI will create the most value. After all, they serve customers in the audience and advertising markets, with external effects on society. At the same time, they are limited by scarce resources, which constrains the implementation of AI. Therefore, based on Porter's value chain, this article pursues two goals. First, drawing on previous research, we provide a systematic overview of activities for which local news organizations see the biggest potential of AI to create value. Moreover, we highlight promising AI use cases based on benchmarking with national news organizations. Second, we discuss local news organizations' challenges in implementing AI and how they might overcome such obstacles. [ABSTRACT FROM AUTHOR]

The public service media Radio Televisión Española (RTVE) conducted a proof-of-concept study to automatically generate reports on the results of the local elections of 28 May 2023 in Spanish communities with fewer than 1000 inhabitants. This study describes the creation, testing and application of the methodological tool used to evaluate the quality of the reports generated using artificial intelligence in order to optimize the algorithm. The application of the proposed datasheet provided a systematic analysis, and the iterative use of the tool made it possible to gradually improve the results produced by the system until a suitable threshold was reached for publication. The study also showed that, despite the ability of AI systems to automatically generate a large volume of information, both human labour and the reliability of the data that feed the system are essential to ensure journalistic quality. [ABSTRACT FROM AUTHOR]

Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in which it is being heavily leveraged are not yet known to the public. To understand how generative AI is reshaping print media and the extent to which it is being implemented already, methods to distinguish human-generated text from that generated by AI are required. Since college students have been early adopters of ChatGPT, we sought to study the presence of generative AI in newspaper articles written by collegiate journalists. To achieve this objective, an accurate AI detection model is needed. Herein, we analyzed university newspaper articles from different universities to determine whether ChatGPT was used to write or edit the news articles. We developed a detection model using classical machine learning and used the model to detect AI usage in the news articles. The detection model showcased a 93% accuracy in the training data and had a similar performance in the test set, demonstrating effectiveness in AI detection above existing state-of-the-art detection tools. Finally, the model was applied to the task of searching for generative AI usage in 2023, and we found that ChatGPT was not used to revise articles to any appreciable measure to write university news articles at the schools we studied. [ABSTRACT FROM AUTHOR]

Data permeates nearly all spheres of society, and journalism is no exception to this since data has become a cornerstone of reality construction and perception. This contribution sets out to historicize the datafication processes in digital journalism and the way in which European institutions of media (self-)regulation have dealt with ethical issues regarding the use of data in algorithmic journalism in three areas: accountability, transparency, and privacy. The article shows that the process of datafication in journalism cannot be observed and analyzed in isolation, given that there is a double reflexivity between data-driven societal transformation processes and what happens in journalism. However, almost all press councils in Europe have so far ignored data-driven phenomena like algorithms or news automation. As a consequence, if self-regulators do not regulate, other institutions will, with the risk of news organizations being forced to make decisions on the grounds of regulatory frameworks that are not primarily intended for journalism. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive bibliometric review of the development of artificial intelligence (AI) in journalism based on the analysis of 331 articles indexed in the Scopus database between 2019 and 2023. This research combines bibliometric approaches and quantitative content analysis to provide an in-depth conceptual and structural overview of the field. In addition to descriptive measures, co-citation and co-word analyses are also presented to reveal patterns and trends in AI- and journalism-related research. The results show a significant increase in the number of articles published each year, with the largest contributions coming from the United States, Spain, and the United Kingdom, serving as the most productive countries. Terms such as “fake news”, “algorithms”, and “automated journalism” frequently appear in the reviewed articles, reflecting the main topics of concern in this field. Furthermore, ethical aspects of journalism were highlighted in every discussion, indicating a new paradigm that needs to be considered for the future development of journalism studies and professionalism.

This study aims to assess the persistence of traditional journalistic quality features within the contemporary digital ecosystem. Digital advancements have fundamentally altered media creation, notably with the emergence of artificial intelligence (AI) in newsrooms. AI holds potential for substantial shifts in journalism, impacting business models, dissemination methods, and professional practices. Present in 75% of newsrooms, AI streamlines tasks, allowing more creative time for journalists. Ethical and quality concerns persist, particularly regarding AI’s ability to meet journalism’s established quality standards. This article aims to investigate the incorporation of these quality criteria in news articles generated by artificial intelligence (AI). Focus group and in-depth interviews were used as methodological techniques, involving ten experts. It is concluded that journalistic ethics have remained intact despite the disruptive technological advances in recent decades. However, there is a need to integrate these ethics with new criteria associated with the tools being used. Therefore, it is necessary to consider criteria from a dual perspective: both social and technological. [ABSTRACT FROM AUTHOR]

Copyright of Index.Comunicación is the property of Index.comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)